065
065
This paper is a product of the Poverty and Equity Global Practice Group. It is part of a larger effort by the World Bank to
provide open access to its research and contribute to development policy discussions around the world. The authors may
be contacted at aatamanov@worldbank.org.
The Poverty & Equity Global Practice Working Paper Series disseminates the findings of work in progress to encourage the exchange of
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or the governments they represent.
‒ Poverty & Equity Global Practice Knowledge Management & Learning Team
This paper is co-published with the World Bank Policy Research Working Papers.
Robustness of Shared Prosperity Estimates: How
Different Methodological Choices Matter
Aziz Atamanov, Christina Wieser, Hiroki Uematsu, Nobuo Yoshida, Minh Cong Nguyen, Joao Pedro
Wagner De Azevedo and Reno Dewina1
JEL codes: I32, D31, D63
Key words: shared prosperity; robustness tests
1
All authors are with the World Bank, Washington DC. Contact email: aatamanov@worldbank.org.
1. INTRODUCTION
The World Bank Group’s (WBG) twin goals of ending extreme poverty and promoting shared prosperity
are at the forefront of the WBG’s operations. The shared prosperity goal, defined as fostering income
growth of the bottom 40 percent, reflects the fact that as countries grow, the benefits of the growth may
not necessarily be widely shared among the population. Nations with a widening gap between those who
can and cannot access opportunities in life have difficulty sustaining economic growth and social stability
over time. Shared prosperity therefore focuses on two, often competing, concerns, expanding the size of
the economy and sharing it among all members of society, particularly the poor (World Bank, 2015a).
Furthermore, the indicator of shared prosperity features prominently as target 10.1 of one of the
Sustainable Development Goals of reducing inequality within and among countries. The concept and
measurement of shared prosperity may therefore gain traction beyond the Word Bank Group’s
operational work in the coming years.
Unlike ending extreme poverty, the shared prosperity goal is country specific without a specific target at
the global level. Good performance depends on whether consumption of the bottom 40 percent of the
population is positive and how it compares with growth of the population from other parts of the
distribution.
The frequency and quality of data on poverty and shared prosperity are of utmost importance to
successfully monitor the twin goals and to set effective policies for a country’s poverty reduction program.
Shared prosperity measures consumption or income growth rates from representative household surveys
and requires a comparable welfare aggregate at two points in time. This makes it particularly demanding
in terms of data availability and quality (World Bank, 2015a).
Countries should make numerous methodological choices to measure shared prosperity. The impact of
these choices on the shared property indicator is an empirical question. This issue becomes even more
important when the monitoring of shared prosperity is done at the global level. For example, the World
Bank constructs corporate numbers on shared prosperity based on welfare indicators used for estimating
poverty headcount rates measured at the $1.90 per day per capita in 2011 PPPs poverty line. This decision
was made to ensure consistency for the two indicators of the WBG measuring extreme poverty and shared
prosperity. As a result, the estimation of the shared prosperity indicator follows special conventions of
adjustments adopted for estimating extreme poverty. Some of these adjustments are not necessarily
consistent with the recommendations of the World Bank’s poverty measurement manuals (Deaton and
Zaidi 2002; Ravallion 1992, 1996), which have been adopted by many developing countries when
estimating national poverty and inequality statistics. It is, therefore, crucial to understand how different
choices in the estimation process affect the shared prosperity indicator.
Given that shared prosperity is a relatively new indicator, there is a lack of studies that systematically test
the robustness of the indicator to different parameters. Most of the existing literature focuses on global
performance in shared prosperity and its relationship with overall growth and inequality (Narayan et al.,
2013; Cruz et al., 2015; World Bank, 2015ab). To the best of our knowledge, there were only a couple of
illustrative tests in the World Bank (2015a) thus far, demonstrating how the choices of data and time
intervals affect the indicator of shared prosperity using the example of Peru. Results of systematic
robustness tests will be useful to the World Bank for a better measurement of its corporate goal and to
countries which may choose to deviate from the methodology used by the World Bank and to introduce
changes to the construction of the indicator of shared prosperity.
2
The key objective of this paper is to systematically explore the robustness of the shared prosperity
indicator to different methodological choices in the estimation process such as: grouped versus
microdata, nominal welfare aggregate versus adjustments for spatial price variation, and different
treatment of income with negative and zero values. The analysis is conducted using household survey
data used for estimating extreme poverty and shared prosperity indices. For most tests, we randomly
selected at least two countries from each of the six regions to reflect global representation. In addition to
robustness tests, we briefly present the most recent results and findings from the Global Database of
Shared Prosperity (GDSP) circa 2007‐2012 to provide context and familiarize the readers with the most
recent trends in shared prosperity in the world.
The paper continues as follows. Section 2 provides a brief overview of the GDSP. In section 3, we focus on
special technical issues in the construction of the shared prosperity index that may affect the obtained
results and empirically test them. Section 4 concludes.
2. AN OVERVIEW OF THE GLOBAL DATABASE OF SHARED PROSPERITY
This section provides a brief overview of data and trends in the Global Database of Shared Prosperity
(GDSP). This is merely a complement to recent publications on shared prosperity such as Cruz et al. (2015),
the World Bank Global Monitoring Report (2015b), and the World Bank Policy Research Report (2015a),
which provide a more exhaustive analysis and results on shared prosperity. The GDSP is a collection of the
shared prosperity index across the world, where the index is defined as the average annualized growth
rate of consumption or income among the bottom 40 percent of the population. The latest version of the
GDSP, published in October 2015,2 has the shared prosperity index for 94 countries, up from 72 in the
version published in September 2014.3 In the latest GDSP, the shared prosperity index is calculated using
household surveys circa 2007–2012. The average years for the first and the second surveys are 2006.6
and 2011.7, respectively.4
With the paucity of household survey data as recently documented in Serajuddin, et al. (2015), it is
remarkable that as many as 94 countries satisfied the substantial data requirements to calculate the
shared prosperity index. Of the 134 countries included in PovcalNet,5 however, only 71 countries (53
percent) are also included in the latest version of the GDSP. Using the World Bank’s regional classification,
three regions are relatively underrepresented in the GDSP. For East Asia and the Pacific, the GDSP has 7
out of 18 countries (39 percent); for the Middle East and North Africa, 4 out of 11 countries (36 percent);
and for Sub‐Saharan Africa, 15 out of 43 countries (35 percent). In the other four regions, the GDSP
contains 45 out of 72 or about 70 percent of the countries in PovcalNet. IDA countries and countries
designated as fragile and conflict affected states are also under‐represented. The GDSP includes about 35
percent of IDA countries and 65 percent of all other countries in PovcalNet. Of the 24 fragile and conflict
affected states in PovcalNet, only five of them are in the GDSP.
Notwithstanding, the GDSP appears to be a fairly good representation of the developing world in terms
of poverty incidence. The 71 countries that are present in both the GDSP and PovcalNet in total account
for approximately 800 million of the poor population and 5.1 billion of the total population,6 both of which
2
Available at: http://www.worldbank.org/en/topic/poverty/brief/global‐database‐of‐shared‐prosperity
3
Most of the increase is due to the addition of high income countries in Europe and Central Asia as well as North
America.
4
For a more detailed description on how the Shared Prosperity indicator is calculated, please refer to Box 1 in the
annex.
5
PovcalNet is the primary source of the World Bank’s international poverty estimates, available at:
http://iresearch.worldbank.org/PovcalNet/
6
Based on figures in the World Development Indicators as of the year of the second survey for each country.
3
are about 85 percent of the developing world total (PovcalNet, 2015). The population weighted average
poverty rate among the 71 countries as of the second survey year is 16.8 percent, fairly close to the
developing world average poverty rate of 15 percent in 2012 (PovcalNet, 2015).
2.1. Growth of the Bottom 40 Percent vs. Total Population
The shared prosperity index and its focus on the bottom 40 percent of the population is a refinement to
the long‐standing and implicit focus on economic growth for the total population as a condition for
poverty reduction (Beegle et al., 2014). In this light, a simple comparison of the two growth rates, one for
the bottom 40 percent and the other for the total population,7 is a natural starting point to understand
the state of shared prosperity. Of the 94 countries in the GDSP, the bottom 40 growth rate was higher
than the total growth rate in 56 countries (Table 1). Of those, 20 countries experienced a bottom 40
growth rate greater than 4 percent a year (top right quadrant).8 These are countries in which strong
economic growth was shared with the poorer segments of the population. An additional 27 countries
shared the gains of economic growth to a larger extent with the poorer segments of the population but
growth overall was lower than 4 percent annually. On the contrary, in 18 countries, the bottom 40 growth
was positive, but lower than the total growth. Finally, 29 countries experienced a negative bottom 40
growth rate and, in more than two‐thirds of these countries, the mean consumption/income of the
bottom 40 percent contracted even more than that for the total population.
Table 1: Income growth of the bottom 40 percent (G40), circa 2007‐2012
Annualized growth in mean consumption or income of bottom 40
Total
Bottom 40 Growth 4%
Bottom 40 Growth >
9 27 20 56
Total Growth
Bottom 40 Growth Figure 1: Bottom 40 Growth Rates and Total Growth Rates, circa 2007‐2012
Source: Global Database of Shared Prosperity, circa 2007‐2012.
2.2. Shared Prosperity Index, Gini, and Poverty
In the latest GDSP, there is a weak but positive correlation between the initial level of the Gini coefficient
and the bottom 40 growth rate (figure 2) with a correlation coefficient of 0.38 (p‐value Figure 2. Gini (1st year) and Bottom 40 Growth Rates
Source: Global Database of Shared Prosperity, circa 2007‐2012.
Figure 3. Changes in Gini and Bottom 40 Growth Rates
6
Source: Global Database of Shared Prosperity, circa 2007‐2012.
As expected, the bottom 40 growth rate is highly correlated with changes in poverty headcount rates
measured at the $1.90 line (see figure 4), with a negative and significant regression coefficient. Countries
with a higher bottom 40 growth rate tend to reduce poverty at a faster pace. Interestingly, the negative
correlation is strong in the ECA region but weak in the poorest region, Sub‐Saharan Africa. Results are
largely unchanged when poverty headcount rates measured at the $3.10 line is used.
Figure 4: Change in poverty ($1.90 PPP 2011) and growth of bottom 40 percent, %
Source: Global Database of Shared Prosperity, circa 2007‐2012.
While the analyses so far are mostly consistent with those in the existing studies on the shared prosperity
index,10 they have been relatively silent on the technical details and the underlying assumptions behind
the calculation of the shared prosperity index. The definition of the shared prosperity index, i.e., the
growth of the average income/consumption of the bottom 40 percent of the population, is deceptively
simple, given that it requires data from two household surveys that must be comparable. Section 3
discusses special technical issues in the construction of the shared prosperity index and tests for
robustness of the index to alternative assumptions.
3. SPECIAL TECHNICAL ISSUES
Corporate numbers of the shared prosperity indicator are estimated by means of the same welfare
indicators used for estimating the extreme poverty headcount rates measured at the $1.90 per capita per
day poverty line. This decision was made to ensure consistency for measuring the two goals of the World
Bank Group, ending extreme poverty and boosting shared prosperity.
However, as a result, the estimation of the shared prosperity indicator follows some special adjustments
adopted for estimating extreme poverty. For example, when estimating the shared prosperity index,
10
For example, see Narayan et al. (2013) and World Bank (2015a, 2015b).
7
welfare indicators are not adjusted to spatial price differences in many countries. The World Bank
however usually recommends spatial price adjustment when measuring poverty and inequality (Deaton
and Zaidi, 2002; Ravallion, 1992, 1996). This is due to the fact that different price levels across
geographical areas represent different levels of consumption and welfare for the same amount of
expenditure.
In the past, PovcalNet, decided not to use spatially deflated household expenditures to facilitate inter‐
temporal and international comparability of extreme poverty indicators. Indeed, the ways spatial price
adjustments are conducted differ largely across countries and are for some countries not properly
documented. Recently however, the documentation of price adjustments has improved and the adopted
methodologies have become more standardized. PovcalNet started to accept spatially adjusted welfare
aggregates but for most countries, the extreme poverty and the shared prosperity indices are still
estimated with spatially unadjusted welfare aggregates. It is important to examine implications of the use
of spatial price adjustments for estimating the shared prosperity index.
Another potential issue is that when calculating shared prosperity for selected countries (and extreme
poverty), mean expenditure or income of the poorest 40 percent of the population are estimated using
grouped data rather than estimating directly from the distribution of the household expenditure or
income. The grouped data approach first calculates means of household expenditure or income for deciles
or ventiles and then estimates a Lorenz curve from the grouped data. Finally, the mean of the bottom 40
percent of the population of a country is estimated from the fitted Lorenz curve and the mean of
household expenditure or income in the whole population (Datt, 1998; World Bank, 2008; Mitra et al.,
2010; or Box 2 in the annex).
The mean of the poorest 40 percent of the population can be directly calculated from the microdata or
grouped data. Creating grouped data and estimating a Lorenz curve using grouped data seems
unnecessary and some might even argue that the additional steps add non‐negligible estimation errors
(Azevedo and Mitra, forthcoming). The grouped data approach was adopted for the monitoring of
extreme poverty because the World Bank could not access household unit record data for most countries
when the monitoring started. Even though PovcalNet has started using microdata for estimating extreme
poverty for most countries since 2015, there are still several large countries such as China, Iran and Algeria
that do not share household unit record data with the Bank. As a result, PovcalNet needs to use the
grouped data approach for these countries to calculate poverty and shared prosperity indicators. This is
currently the case for 15 out of 94 countries.
The last technical issue is associated with the use of income for measuring shared prosperity. There is a
clear preference of using consumption data for monitoring extreme poverty, because it has several
advantages over income data in terms of welfare measurement (Deaton, 1997). Nevertheless, many
middle income countries in LAC and ECA use income data for their official poverty measurement. For the
2014 and 2015 update of the GDSP, the GPWG decided to increase the number of countries for which
income, rather than consumption, is used to estimate shared prosperity.
However, the distribution of income is quite different from that of consumption expenditure and thus
additional problems, which consumption data did not have, may occur by moving from consumption to
income. For example, income data are usually more volatile than consumption. Of particular concern is
income with negative or zero values. Such outliers can affect inequality statistics and means of the poorest
40 percent, resulting in a “lumpy” movement of the shared prosperity indicator.
Empirically testing the impact of different methodological choices in this section is based on a different
set of countries depending on the type of test conducted. Comparing grouped versus microdata is based
on 20 countries representing all regions of the World. Sixteen countries were used to estimate the impact
of having spatially adjusted versus nominal welfare aggregates. The latter analysis includes fewer
8
countries because PovcalNet does not yet use spatially adjusted welfare aggregates for many countries.
Finally, the issue of negative and zero incomes is only relevant for countries of the European Union and
Latin American and Caribbean (LAC). In LAC, negative incomes are recoded as missing and therefore, the
EUSILC database was used to test the impact of negative incomes, while LAC countries were used to test
the impact of zero incomes. The sections below investigate how changes in these particular adjustments
may affect the shared prosperity index.
3.1. Grouped versus Microdata
There is a systematic difference between estimates using grouped and microdata of mean consumption
per capita of the bottom 40 percent. In almost all cases, except the first year in Armenia and the second
year in Peru, means from grouped data are lower than means from microdata. The difference ranges from
a very small negative 0.12 percent in the case of Iraq to negative 1.41 percent in the case of Rwanda.
Given that the impact of using grouped data affects both means in two periods of time, the indicator of
shared prosperity is not expected to be very much affected. In a way, the effect is similar to multiplying
means in the shared prosperity formula by a constant number, which should not affect the rates of
growth.
Table 2. Grouped and microdata based daily mean consumption per capita of the bottom 40 percent
of the population in $/PPP 2005 and difference between them in percent
difference in %:
micro grouped micro grouped
(grouped/micro‐1)*100
b40 consumption, t1 b40 consumption, t2 t1 t2
Rwanda 14.1 13.9 17.7 17.5 ‐1.4 ‐1.2
Colombia 64.7 64.0 83.2 82.4 ‐1.1 ‐1.0
Madagascar 12.8 12.6 10.1 10.1 ‐1.0 ‐0.8
Dominican Republic 78.2 77.5 87.7 87.2 ‐0.9 ‐0.6
Cambodia 32.1 31.8 45.6 45.2 ‐0.9 ‐0.7
Brazil 79.2 78.5 104.5 102.9 ‐0.9 ‐1.5
Bolivia 47.9 47.5 87.2 86.5 ‐0.7 ‐0.9
Congo 18.3 18.1 27.7 27.6 ‐0.7 ‐0.3
Sri Lanka 51.8 51.5 56.5 56.2 ‐0.7 ‐0.5
Russia 120.0 119.5 189.5 188.1 ‐0.5 ‐0.7
Kazakhstan 96.7 96.2 122.8 122.4 ‐0.4 ‐0.3
Indonesia 36.0 35.8 39.5 39.5 ‐0.4 0.0
Paraguay 64.3 64.1 92.5 92.3 ‐0.4 ‐0.1
Pakistan 36.2 36.1 42.0 41.9 ‐0.4 ‐0.2
Tunisia 88.1 87.8 104.5 104.1 ‐0.3 ‐0.3
West Bank and Gaza 133.2 132.8 149.1 148.6 ‐0.3 ‐0.4
Peru 69.1 68.9 101.4 101.6 ‐0.2 0.2
Spain 431.9 431.3 354.2 352.8 ‐0.1 ‐0.4
Iraq 57.1 57.0 58.1 58.0 ‐0.1 ‐0.2
Armenia 60.5 60.6 61.9 61.8 0.1 ‐0.1
Source: Microdata provided by regional focal points, members of the Global Poverty Working Group. Authors’ calculations.
Here and thereafter, the POVCAL11 command was used to get grouped data estimates.
Notes: Grouped data are based on ventiles.
Shared prosperity estimates calculated from micro and grouped data are shown in figure 5. Overall, the
differences are not substantial in absolute terms (third column in the table of figure 5). The largest
differences in grouped and microdata growth rates are observed for Peru followed by Brazil. However,
11
POVCAL is developed by Qinghua Zhao who kindly shared it with the team.
9
they do not exceed 0.15 percentage points in absolute terms. Expectedly, the picture changes if the
difference is considered in relative terms (figure 5). Armenia, Indonesia and Iraq have the largest
differences due to low growth rates of the bottom 40 percent. In particular, consumption per capita
growth rates for the bottom 40 based on microdata in Armenia is almost 14 percent higher than the
estimate based on grouped data.
Even though in absolute terms the difference between grouped and microdata is the largest for income
welfare aggregates in Brazil and Peru, overall there is no indication that the average difference between
grouped and microdata estimates of shared prosperity is higher for income than consumption (of 20
countries, seven measure welfare by income: Dominican Republic, Bolivia, Colombia, Brazil, Paraguay,
Peru, and Spain). The average difference in the shared prosperity indicator is about (‐0.02) percentage
points and 0.79 percent for countries using consumption compared to (‐0.01) percentage points and (‐
0.49) percent for countries using income per capita.12
Figure 5. Difference between grouped and microdata estimates of growth rates among the bottom
40 percent of the population in percentage points and percent
Source: Microdata provided by regional focal points, members of the Global Poverty Working Group. Authors’ calculations.
Notes: Grouped data are based on ventiles.
12
More systematic research is needed to explore this issue. Using information for countries which measure
welfare using both income and consumption will facilitate the process.
10
3.2. Spatial Deflation versus Nominal
3.2.1 Impact on shared prosperity
Many countries adjust welfare aggregates for spatial variation in prices for poverty measurement. Using
nominal versus spatially deflated welfare aggregates may affect the shared prosperity estimates. To test
this, we calculated average annualized growth rates for the total population and the bottom 40 percent
based on nominal and spatially deflated welfare aggregates in 18 countries. Results are presented in figure
6. As can be seen, the differences in growth rates are not significant in most countries except Madagascar
and Rwanda, where consumption per capita growth rates either move in opposite directions or differ
substantially depending on the choice of welfare aggregate, whether it is nominal or spatially deflated.
Figure 7 provides hints on the source of these differences. For most countries in our database, population
weighted means of the spatial price index remain constant around one, while for Madagascar, Rwanda,
and Vietnam, the means in both rounds clearly divert from one. It is not necessarily peculiar to see a mean
of the spatial price index different from one. This occurs if the reference prices are selected from a
particular area rather than drawn from national average prices and we then have a population weighted
mean of the corresponding spatial price index different from one. Indeed, some countries choose
reference prices from the capital city and in these cases, the population weighted mean of the
corresponding spatial price index is usually below one because prices from the capital cities are higher
than the national average prices. In the case of Madagascar, Rwanda, and Vietnam, the mean spatial price
index is higher than one. This means that these countries chose reference areas with lower prices than
the national average.
Whether the population weighted average of the spatial price index is equal to one affects the mean of
the spatially deflated welfare indicator. If areas with prices lower than the national average are selected
as reference areas, the mean of the spatial price index will be above one and the mean of deflated welfare
indicator will be lower than that of a nominal welfare indicator. An important fact is that as long as the
same reference areas are selected, the selection of reference areas will not affect the growth rate of
means. This is indeed the case for Vietnam. The means of the spatial price index are systematically higher
than one in Vietnam, but the level did not change over time. As a result, the growth rate of spatially
deflated means is almost identical to that of un‐adjusted means.
However, if the reference area changes over time, the growth rate of means of the spatially deflated
welfare indicator will include the actual growth rate and the effect of changing the reference areas or
more precisely reference prices. For both Madagascar and Rwanda, the mean of the spatial price index
increased significantly between the two rounds of surveys. As a result, the growth rate of spatially
adjusted means is the actual growth rate minus the effect of choosing lower prices as reference (or an
increase in the mean spatial price index). Indeed, in the case of Rwanda, the growth rate of un‐adjusted
means is 4.5 while the growth rate of spatially adjusted means is ‐2.7 percent (see figure 6). It is likely that
a large part of the large reduction in the growth rate should be attributed to the increase in the mean of
the spatial price index between two rounds. The same applies for Madagascar.
11
Figure 6. Consumption per capita growth for the bottom 40 and total population across nominal and
spatial welfare aggregates, %
Source: Microdata provided by regional focal points, members of the Global Poverty Working Group. Authors’ calculations.
Figure 7. Average spatial deflators for two periods across countries
Source: Microdata provided by regional focal points, members of the Global Poverty Working Group. Authors’ calculations.
Notes: Deflators are spatially weighted.
12
3.2.2 Profile of the bottom 40 percent of population
In the previous section, we observed that if spatial deflators are measured consistently across two periods
in time, the impact on shared prosperity indicators is rather minimal for most countries unless the spatial
price adjustments were carried out inconsistently over time. Even if growth rates do not change, it is still
important to adjust for spatial price differences because purchasing power of the same amount of
expenditure or income differs significantly if the price level differs. Furthermore, the welfare ranking of
people can change significantly based on spatial price adjustments. Spatially adjusted prices may change
where certain people in the bottom 40 percent are located in the welfare distribution. We explore this
concern empirically using a subsample of our database – data for Kazakhstan, Rwanda, Tajikistan and Iraq.
Table 3 shows the percentage of the population remaining in the same decile regardless of the choice of
welfare aggregate: nominal or spatially deflated. Substantial reshuffling of people occurs starting from
the second decile. In Rwanda for example, only 60 percent of the population in the fourth decile stays if
the welfare aggregate is spatially deflated. A further 20 percent of those are found in the fifth decile if
regional variation in prices is taken into account.
As a result of the change in rankings, the share of the population in the bottom 40 can shift across regions.
This is illustrated in figure 8 using data for Kazakhstan. In ten of sixteen regions, the share of the
population from the bottom 40 percent is significantly different depending on whether a nominal or
spatially deflated welfare aggregate is used. For example, only 8 percent of the total population in Almaty
was from the bottom 40 percent if nominal consumption per capita is used; however, the ratio increases
to 14 percent if the welfare aggregate is spatially deflated (results for Iraq and Rwanda are provided in
the annex).
Table 3. Percentage of population staying in the same decile regardless of welfare aggregate: spatially
deflated or nominal
Deciles Kazakhstan, 2010 Iraq, 2012 Rwanda, 2010
bottom 89 86 90
2 72 66 72
3 62 57 64
4 57 54 60
5 56 49 58
6 57 49 57
7 60 55 61
8 65 59 69
9 76 68 81
top 91 88 93
Source: Microdata provided by regional focal points, members of the Global Poverty Working Group. Authors’ calculations.
13
Figure 8. Share of population from bottom 40 percent across regions and type of welfare aggregate,
Kazakhstan 2010
70%
60%
50%
40%
30%
20%
10%
0%
***Almaty
***Almaty_city
Aktobe
West_Kaz
Karaganda
Pavlodar
Akmola
***Atyrau
***Mangystau
***South_Kaz
North_kaz
**East_Kaz
***Astana_city
**Kostanay
***Kyzylorda
**Jambyl
nominal deflated
Source: Microdata provided by regional focal points, members of the Global Poverty Working Group. Authors’ calculations.
Notes: * significant difference in proportions test at 10 percent, ** at 5 percent, *** at 1 percent level.
In addition to changes in the distribution of the population in the bottom 40 percent, the choice of welfare
aggregate can also affect the individual profile of the population in the bottom 40. However, as shown in
table 4, the difference is small using the example of individual characteristics in Tajikistan. The impact may
be stronger if people are very different across the fourth, fifth and sixth deciles where the largest
reshuffling takes place.
Table 4. Individual characteristics of bottom 40 percent in Tajikistan across type of welfare aggregate
b40, deflated welfare aggregate b40, nominal welfare aggregate
Education level, 15+
Basic education 22.2 21.9
General secondary education 54.4 55.2
Special secondary education 8.7 8.6
Tertiary education 6.5 6.3
None/less than primary education 8.2 8.1
Labor force status, 15+
Employed 17.1 16.7
Self‐employed 23.6 23.3
Unemployed 5.0 4.9
Retired 9.0 8.8
Student 7.8 7.6
Other labor force status 37.6 38.8
Source: Microdata provided by regional focal points, members of the Global Poverty Working Group. Authors’ calculations.
Theoretically, spatial price adjustments have an impact on the welfare ranking of households and on
growth rates of household income and expenditure. However, according to our empirical analysis, unless
spatial adjustments were conducted inconsistently over time, the impact of spatial price adjustments on
growth rates including those of the bottom 40 percent is minimal. However, carrying out spatial price
14
adjustment has significant impact on the welfare ranking of households, individuals and areas. These
rankings are important for designing policies to alleviate extreme poverty and promote shared prosperity.
Unadjusted welfare indicators will likely misguide the identification of the extreme poor and the poorest
40 percent of the population.
3.3. Shared Prosperity Using Income Data
As mentioned, various factors in the poverty measurement literature point to consumption being a better
proxy to measure welfare than income. Nevertheless, many countries in the world measure welfare using
income. In particular, European countries measure welfare using income data from Eurostat on Income
and Living Conditions (EUSILC) including negative and zero income. Countries from LAC also use income
to measure social‐economic well‐being, but recode negative income to missing values. In this section, we
test the impact of negative income data on shared prosperity using EUSILC data and the impact of zero
incomes on shared prosperity using selected countries from the Latin American and Caribbean region.13
3.3.1 Data with negative numbers
The shares of households with negative income across EUSILC surveys are minimal, with a maximum of
1.2 percent for Greece in 2011. For most countries collecting EUSILC data, the share is less than 0.5
percent. To observe the impact of negative numbers of income, we run three simulations: (1) keeping
negative values; (2) replacing negative values with zero; and (3) excluding negative values. The results of
the shared prosperity numbers using these three scenarios are reported in table 5. The differences in
estimates for the proposed scenarios are reported in percent and percentage points. In relative terms,
the difference in estimates can vary substantially reaching more than 100 percent; however, this is mainly
for countries with low numbers on shared prosperity. In absolute terms, the differences are less dramatic
ranging from 0 to 0.7 percentage points.
Though not strictly comparable (as different sets of countries are used), the magnitude of the change in
shared prosperity estimates after excluding negative numbers seems to be higher compared to the results
for grouped versus microdata. For example, exclusion of negative income can increase shared prosperity
by 59 percent in the case of Belgium and reduce it by 123 percent in the case of Denmark, whereas the
largest change in shared prosperity using microdata did not exceed 14 percent. In absolute terms,
excluding negative incomes also has also a higher impact than the choice of grouped versus microdata.
Thus, the largest gap for testing the effects of negative income on shared prosperity is 0.7 percentage
points compared to 0.14 percentage points for grouped versus microdata (table of figure 5).
The largest absolute differences in estimates (in percentage points) are observed in countries with the
highest shares of negative numbers (figure A3) and large negative numbers. For example, in Denmark
(income year 2011), 25 percent out of 24 households (0.44 percent of the 5,355 household sample) with
negative incomes (per capita per day in 2005 PPP terms) are very large, ranging from ‐20 to ‐400, while
the weighted national average is 39 and the maximum is 541. This suggests that the frequency of negative
numbers and their magnitude can seriously distort the shared prosperity indicator.
13
In addition, we use the opportunity that EUSILC data have both, negative and zero incomes, and compare
impacts of dropping these observations. Results are shown in the annex. As one may expect, dropping negative
observations has stronger impact than dropping observations with zero incomes on average.
15
Table 5. Shared prosperity numbers for three simulation scenarios (negative values) based on EUSILC
data
shared prosperity indicator, % ∆
negative zeroes replace excluding change in %,( Absolute
numbers negative negative (3)/(1)‐ difference in
country code base end (1) numbers (2) numbers (3) 1))*100 pp, (3)‐(1)
Austria 2006 2011 0.71 0.71 0.71 ‐1 ‐0.01
Belgium 2006 2011 0.20 0.29 0.33 59 0.12
Bulgaria 2007 2011 1.40 1.39 1.39 0 0.00
Cyprus 2006 2011 ‐1.14 ‐1.14 ‐1.14 0 0.00
Czech Republic 2006 2011 1.85 1.85 1.85 0 0.01
Denmark 2006 2011 ‐0.57 ‐0.25 0.13 ‐123 0.70
Estonia 2006 2011 1.40 1.43 1.51 8 0.11
Finland 2006 2011 1.97 1.97 1.98 0 0.00
France 2006 2011 3.30 3.24 3.15 ‐4 ‐0.14
Germany 2006 2011 ‐6.16 ‐6.14 ‐5.60 ‐9 0.56
Greece 2006 2011 ‐0.54 ‐0.54 ‐0.53 ‐1 0.00
Hungary 2006 2011 ‐3.09 ‐3.05 ‐2.95 ‐5 0.15
Iceland 2006 2011 ‐3.90 ‐3.90 ‐3.90 0 0.00
Ireland 2006 2011 ‐0.78 ‐0.82 ‐0.98 27 ‐0.21
Italy 2006 2011 0.35 0.37 0.51 47 0.16
Latvia 2006 2011 1.07 1.08 1.18 10 0.11
Lithuania 2006 2011 ‐1.70 ‐1.54 ‐1.43 ‐16 0.27
Luxemburg 2006 2011 1.18 1.11 0.92 ‐22 ‐0.26
Netherlands 2006 2011 4.80 4.60 4.37 ‐9 ‐0.43
Norway 2006 2011 5.62 5.61 5.60 0 ‐0.02
Poland 2006 2011 0.07 0.07 0.07 0 0.00
Portugal 2006 2011 1.58 1.48 1.43 ‐10 ‐0.16
Romania 2006 2011 3.40 3.40 3.57 5 0.17
Slovakia 2006 2011 8.40 8.40 8.39 0 ‐0.01
Slovenia 2006 2011 1.47 1.40 1.39 ‐5 ‐0.08
Spain 2006 2011 ‐0.81 ‐0.73 ‐0.57 ‐29 0.24
Sweden 2006 2011 2.51 2.62 2.70 8 0.20
Switzerland 2007 2011 2.04 2.00 1.92 ‐6 ‐0.11
United Kingdom 2006 2011 ‐1.27 ‐1.10 ‐1.02 ‐19 0.24
Source: EUSILC, microdata provided by regional focal points, members of the Global Poverty Working Group. Authors’
calculations.
3.3.2 Data with zeroes
Shares of the population with zero incomes in selected countries from LAC do not exceed 1.4 percent. In
order to test the impact of having a population with zero income on shared prosperity we have run two
scenarios in this sub‐section: (1) keeping people with zero income; (2) excluding people with zero income
per capita. Composite figure 9 shows the annualized growth rates for the bottom 40 percent. The
differences in estimates for selected scenarios are reported in percentage points in two sub‐figures in the
bottom part of figure 9. We also plot sum of shares of the population with zero income in two periods for
all countries.
In absolute terms (though not strictly comparable), the impact of excluding zero income is stronger than
the choice of grouped versus microdata, but weaker than the impact of excluding negative income. Thus,
the largest gap for excluding zero income is 0.38 percentage points in absolute terms (Brazil) compared
to the highest 0.7 percentage points obtained from excluding negative income (table 5) and highest 0.14
percentage points obtained from grouped versus microdata test (figure 5).
16
Figure 9. Shared prosperity numbers for two scenarios including and excluding zero income
Shared prosperity growth rates in selected LAC countries including and excluding zero income, 2006-2011
Period Country with zeros without zeros
2006-2011 Bolivia 12.66 12.36
Peru 9.94 9.94
Paraguay 7.53 7.53
Brazil 5.71 6.08
Ecuador 4.35 4.43
Honduras 4.23 4.41
Chile 3.90 3.91
Dominican Repub.. 2.32 2.29
difference in growth rate of bottom 40, pp total share of zero income earners
Difference in growth rate of bottom 40 and share of population with zero income
1.6 2.0
difference in growth rate of bottom 40, pp
1.5
total share of zero income earners
1.2
0.8 1.0
0.4 0.5
0.0 0.0
-0.4
Ecuador
Brazil
Chile
Peru
Honduras
Paraguay
Republic
Dominican
Bolivia
Source: SEDLAC, microdata provided by regional focal points, members of the Global Poverty Working Group. Authors’
calculations.
Expectedly, larger absolute differences in obtained estimates (in percentage points) are observed in
countries with higher shares of the population with zero incomes. However, the magnitude of the
population with zero income is not the only factor behind the impact of excluding zero income earners.
For example, impact is stronger in Bolivia than in Ecuador (bars in figure 9) even though the share of zero
earners is higher in Ecuador than in Bolivia (lines in figure 9). This is a result of the changes in shares across
years and as a result, excluding zero earners affects means differently across time. This cannot be clearly
observed in figure 9 because it sums up the shares across two years, but table 6 shows shares of the
population with zero income by years. The impact of excluding households with zero income is higher in
Bolivia than in Ecuador because the share of zero earners drops in Bolivia from 0.49 percent in 2006 to
0.17 percent in 2011. At the same time, the share of zero earners in Ecuador remains relatively stable
ranging from 0.64 to 0.75 percent.
17
Overall, there is no clear pattern in the selected LAC countries in terms of the population with zero
incomes across years. For example, in Ecuador there was a steady decline in the share of such a
population, while in Brazil, in contrast, the share was increasing after 2008. In other countries, shares
fluctuated across years without a clear pattern. Changing shares of income earners can be a result of
survey improvement or structural changes affecting shares of the self‐employed in the population.
Irrespective of the reasons behind changes in the magnitude of the population with zero incomes, it is
important to keep in mind that the impact of excluding zero earners on shared prosperity depends both
on the overall magnitude of zero earners and fluctuation across reference years.
Table 6. Share of population with zero income across countries and years in LAC
year Peru Ecuador Chile Brazil Bolivia
2003 0.06 1.15 0.16 1.24
2004 0.01 1.02 0.16
2005 0.00 1.22 0.38
2006 0.00 0.64 0.11 0.72 0.49
2007 0.00 0.92 0.34
2008 0.01 0.90 1.01 0.16
2009 0.01 0.83 0.23 0.47
2010 0.00 0.69
2011 0.00 0.75 1.20 0.17
2012 0.01 0.62 0.29
2013 0.00 0.45 0.15 1.44 0.26
Source: SEDLAC, microdata provided by regional focal points, members of the Global Poverty Working Group. Authors’
calculations.
4. CONCLUSIONS
Measuring the World Bank Group’s goal of shared prosperity is demanding in terms of access to and
quality of microdata. Currently, corporate numbers on shared prosperity are estimated based on
adjustments used for calculating international poverty headcount rates to ensure consistency for the two
goals of the World Bank Group. This paper, for the first time, systematically explores how sensitive shared
prosperity estimates are to the changes in these particular adjustments.
Empirical research reveals that the impact of applying grouped versus microdata has a minimal effect on
estimates of shared prosperity. The means for the bottom 40 percent of the population obtained from
grouped and microdata can differ substantially. However, given that the shared prosperity indicator is
calculated for two points in time, the impact on the consumption per capita growth rate is much smaller
if the differences in means are similar across two points in time. There is tentative evidence that the
difference between grouped and microdata does not depend on the source of welfare aggregate ‐ income
or consumption‐ but this requires more rigorous testing. Overall, the issue of using grouped versus
microdata is less of a concern because the World Bank Group has started using microdata for corporate
poverty and shared prosperity estimates since 2015.
Using a spatially adjusted or nominal welfare aggregate for measuring shared prosperity does not appear
to have a substantial impact on the indicator itself, but can change the distribution. We did not discover
significant differences in the shared prosperity indicator calculated based on nominal or spatially deflated
welfare aggregates. However, it is crucial to control whether the price deflator is calculated consistently
across years. Even small differences in average deflators across years may cause large discrepancies
between estimates. Deflators can differ across years if the base area for the calculation of the price index
changes over time.
18
Besides differences in the shared prosperity indicators per se, using a spatially deflated welfare aggregate
affects the ranking of people. This can change the distribution of the population in the bottom 40 percent
across locations and their characteristics. As an example, we show that in ten of the sixteen regions in
Kazakhstan, the share of the population from the bottom 40 percent varies substantially depending on
the type of welfare indicator chosen.
In many countries, shared prosperity is measured by income. Income tends to be more volatile than
consumption and income may include zero and negative numbers. Empirical tests based on EUSILC data
and data for LAC countries show that the shares of households with negative and zero incomes rarely
exceed 1 percent. Nevertheless, excluding negative and zero numbers affects the estimates on shared
prosperity. Even though the difference between estimates does not exceed one percentage point for any
of the countries for each test, the impact depends on the shares of households with negative/zero income,
its distribution across years and the magnitude of negative values.
This paper conducted a series of robustness tests of shared prosperity to different methodological
assumptions. Further systematic analysis of such tests such as sensitivity to changes in reference periods,
income versus consumption data, and revisions of microdata can be useful.
19
5. REFERENCES
Azevedo, J.P. & Mitra, S. (forthcoming). Global poverty estimation theoretical and empirical validity of
parametric Lorenz curve estimates and revisiting nonparametric techniques. World Bank, Mimeo.
Beegle, K., Olinto, P., Sobrado, C., Uematsu, H., Kim, Y. S., & Ashwill, M. (2014). Ending Extreme Poverty
and Promoting Shared Prosperity. Could There Be Trade‐offs Between These Two Goals? Inequality in
Focus, 3(1). World Bank, Washington, DC.
Cruz, M., Foster, J., Quillin, B. and Schellekens, P. (2015). Ending Extreme Poverty and Sharing Prosperity:
Progress and Policies. Policy Research Note 101740. World Bank, Washington, DC.
Datt, G. (1998), “Computational Tools for Poverty Measurement and Analysis,” FCND Discussion Paper
No. 50.
Deaton, A. & Zaidi, S. (2002). "Guidelines for Constructing Consumption Aggregates for Welfare Analysis,"
World Bank Publications, The World Bank, number 14101, November.
Deaton, A. (1997). The Analysis of Household Surveys: A Microeconometric Approach to Development
Policy. Baltimore, MD: Johns Hopkins University Press.
Dollar, D. T. Kleineberg, Kraay, A. (2013). “Growth Still is Good for the Poor.” Policy Research Working
Paper 6568. World Bank, Washington, DC.
Global Poverty Working Group 2015. “Methodology for computing the indicator on Shared Prosperity for
the Global Database of Shared Prosperity (GDSP) circa 2007–2012”, available at:
http://www.worldbank.org/content/dam/Worldbank/poverty/GDSP_Methodology_Sep2015%20(2).doc
x.
Mitra, S. R. Katayama, and Yoshida, N. (2010). “A Short Note on International Poverty Estimation,” mimeo.
Narayan, A., Saavedra‐Chanduvi J. & Tiwari, S. (2013). “Shared Prosperity: Links to Growth, Inequality and
Inequality of Opportunity”. Policy Research Working Paper 6649. The World Bank.
PovcalNet (2015). PovcalNet: an online analysis tool for global poverty monitoring, available at:
http://iresearch.worldbank.org/PovcalNet/
Ravallion, M. (1992). "Poverty Comparisons ‐ A Guide to Concepts and Methods," Papers 88, World Bank
‐ Living Standards Measurement.
Ravallion, M. (1996). "Issues in Measuring and Modelling Poverty," Economic Journal, Royal Economic
Society, vol. 106(438): 1328‐43.
Serajuddin, U., Uematsu, H., Wieser, C., Yoshida, N., and Dabalen, A. (2015). “Data Deprivation: another
deprivation to end“. Policy Research Working Paper 7252. The World Bank.
World Bank. (2008). Poverty data: A supplement to the World Development Indicators, 2008. World Bank.
World Bank. (2015a). A Measured Approach to Ending Poverty and Boosting Shared Prosperity: Concepts,
Data, and the Twin Goals. Policy Research Report. Washington, DC: World Bank. doi:10.1596/978‐1‐4648‐
0361‐1.
World Bank Group. (2015b). Global Monitoring Report 2014/2015: Ending Poverty and Sharing Prosperity.
Washington, DC: World Bank. doi:10.1596/978‐1‐4648‐0336‐9.
20
ANNEX
Box 1. Creation of the Global Database of Shared Prosperity (GDSP), circa 2007—2012
This box describes the dataset and methodology of the GDSP and draws heavily from the methodology
note “Methodology for computing the indicator on Shared Prosperity for the Global Database of Shared
Prosperity (GDSP) circa 2007–2012” published alongside the GDSP14 and a paper by Narayan, Saavedra‐
Chanduvi and Tiwari (2013). The GDSP includes the most recent figures on annualized consumption or
income growth of the bottom 40 percent and related indicators for 94 countries, which are roughly
comparable in terms of time period and interval.
Choice of surveys, years and countries
The indicator on shared prosperity, measured as the average annualized growth rate of real per capita
income or consumption of the bottom 40 percent of the population (or G40), relies on the availability
of household income or consumption data provided in household surveys. While all countries are
encouraged to estimate G40, the GDSP only includes a subset of countries that have data on income or
consumption readily available and that meet certain considerations. The first important consideration
for creating a global database is comparability across time and across countries. Given that these
numbers would need to be compared within each country over time and across countries for (roughly)
the same period, comparability along both dimensions will be critical. There are limits to such
comparability since household surveys are infrequent in most countries and are not aligned across
countries in terms of timing. Consequently, comparisons across countries or over time should be made
with a high degree of caution.
The second consideration is the coverage of countries, with data that is as recent as possible. Since
shared prosperity must be reported at the country level, there is good reason to obtain as wide a
coverage of countries as possible, regardless of their population size. Moreover, as the utility of this
database depends on how current the information is, using as recent data as possible for each individual
country is important.
The criteria for selecting survey years and countries must be consistent and transparent, and should
achieve a balance between competing considerations: (i) matching the time period as closely as
possible across all countries, while including the most recent data; and (ii) ensuring the widest possible
coverage of countries, across regions and income levels. Achieving any sort of balance between (i) and
(ii) implies that periods will not perfectly match across countries. While this suggests that G40 across
all countries in the database will not be “strictly” comparable, the compromise is worth making to
create a database that includes a larger set of countries.
Construction of GDSP circa 2007–2012
Growth rates in the GDSP are computed as annualized average growth rate in per capita real
consumption or income over a roughly 5‐year period. For the 2015 update, the rules for selecting the
initial survey year (T0) and final survey year (T1) are as follows:
i. The most recent household survey available (year T1) is selected for a country, provided it is
not before 2010.
ii. The initial year (year T0) is selected as close to (T1 ‐ 5) as possible, with a bandwidth of +/‐ 2
years; thus the gap between initial and final survey years ranges from 3 to 7 years.
14
The methodology note can be found at:
http://www.worldbank.org/content/dam/Worldbank/poverty/GDSP_Methodology_Sep2015%20(2).docx
21
iii. If two surveys are equally distant from (T1 ‐ 5), ceteris paribus, the more recent survey year is
selected as T0.
iv. The comparability of welfare aggregates (consumption or income) for the chosen years T0 and
T1 is assessed for every country.15 If comparability across the two surveys is a major concern for
a country, the earlier three criteria are re‐applied to select the next best survey year(s).
Countries that do not have surveys that meet rules (i) to (iv) above are excluded from the GDSP. Even
though all countries are encouraged to estimate G40 using their available data, some countries may be
excluded from the GDSP to maintain some degree of comparability of G40 across countries. For
countries that do meet the rules above, G40 is computed by: (a) estimating the average real per capita
household income of the bottom 40 percent of the consumption or income distribution in years T0 and
T1; and (b) computing the annual average growth rate between these years.16 Growth of average per
capita household income of the population is computed similarly, replacing the bottom 40 percent with
the total population. The mean consumption or income figures are expressed in terms of purchasing
power adjusted dollars per day at 2011 prices (2011 PPP dollars). Annualized growth rates are
calculated between the survey years, using a compound growth formula.17
The GDSP is known as “GDSP circa 2007—2012” because when the GDSP database was created, 2012
was the middle of the range of the final survey years (2010 to 2015). Furthermore, the ideal interval of
the two surveys used for estimating the shared prosperity index is five years and in the 2015 update
was 2007—2012.
15
Strictly speaking there is no clear‐cut procedure or metric to assess comparability. Rather it is judged by poverty
economists in each country who are most knowledgeable about household surveys in that country based on
several criteria (data quality, survey questionnaire design, methodology used for constructing the welfare
aggregate and poverty lines).
16
How exactly step (a) is carried out depends on whether micro or grouped data are used for the calculation. In
2015, PovcalNet changed to mainly using microdata but for some countries (15 out of 94) only grouped data are
available and therefore used in the estimation of shared prosperity. With microdata the steps to calculate average
consumption or income of the bottom 40 percent are simple: sort households by per capita household consumption
or income to identify the bottom 40 percent, and compute the average per capita consumption or income of this
group, weighting per capita consumption or income by household size and sample weights as appropriate. For a
more detailed discussion on grouped vs. microdata, please refer to section 3.1.
17
The annualized growth rate is computed as 1
22
Box 2. Illustration of how to Table A1: An example of a grouped data – Monthly
estimate a poverty rate from household expenditure per capita data in Rural India 1983
grouped data Percentage Mean household
Ranges (Rs) of expenditure per p L
This annex takes us through the individuals capita (Rs)
step‐by‐step procedure for 0 – 30 0.92 24.84 0.92 0.00208
estimating poverty rates from 30 – 40 2.47 35.80 3.39 0.01013
grouped data by way of an example. 40 – 50 5.11 45.36 8.50 0.03122
This example is based on the 50 – 60 7.90 55.10 16.40 0.07083
estimated poverty rates from
60 – 70 9.69 64.92 26.09 0.12808
grouped data for rural India 1983
70 ‐85 15.24 77.08 41.33 0.23498
available in Datt (1998).
85 ‐ 100 13.64 91.75 54.97 0.34887
Grouped data need to include 100 ‐ 125 16.99 110.64 71.96 0.51994
multiple expenditure (or income)
125 ‐ 150 10.00 134.9 81.96 0.64270
ranges, which are ordered by size,
percentage of individuals for each 150 ‐ 200 9.78 167.76 91.74 0.79201
range, and mean household 200 ‐ 250 3.96 215.48 95.70 0.86967
expenditure (or income). Table A1 250 ‐ 300 1.81 261.66 97.51 0.91277
has 13 different ranges of monthly 300 and
2.49 384.97 100 1.00000
household expenditure per capita above
Source: Datt (1998)
for rural India for 1983, which are Notes: p = cumulative proportion of population; L = cumulative proportion of
ordered by size. This table also consumption expenditure
includes the proportion of population and the mean household expenditure for each range. Note that
these expenditure groups do not have the same population share.
The next step is to calculate cumulative proportions of population (p) as well as consumption
expenditures (L) from the grouped data. The columns for p and L in Table A1 represent the results of
this calculation, which include 13 data points of (p, L). From these data points, the Lorenz curve and its
slope are estimated.
Selection of functional forms
To calculate the slope (or the first derivative) of the Lorenz curve, the Lorenz curve is estimated using
one of the following two functional forms – the Beta Lorenz curve and the General Quadratic (GQ)
Lorenz curve. Estimating the Lorenz curve means estimating parameters of a function. For example, if
the Beta Lorenz Curve 1 were used, three parameters , , and need to be
estimated.
Calculating a poverty line
$1.90 and $3.10 poverty lines are valued in 2011 US dollars. These lines need to be converted to local
currency for the particular year. For this, we first need to convert US dollars to local currency using PPP
conversion factors. This will give us a poverty line in local currency of 2011. If the survey year is not
2011, the poverty line needs to be adjusted for inflation. Inflation rates are calculated from Consumer
Price Index (CPI) data available in the World Development Indicators database (WDI).
Calculating a poverty rate from the formula
A poverty headcount rate (H) is calculated by solving the following equation:
/ at (1)
23
where ′ refers to the first derivative of the Lorenz curve, p is the cumulative proportion of population,
z is the poverty line, and is the mean household expenditure (or income).
If the Beta Lorenz curve is adopted, the equation (1) becomes:
1 1 (2)
Equation (2) clearly indicates that if we have the three parameters of the Lorenz curve, the poverty line
and the mean household expenditure (or income), we can solve this equation to get the estimate of
the poverty headcount rate (H). Poverty gaps, severity of poverty, and Gini coefficients can also be
calculated from specific equations derived from the Lorenz curves (see Datt [1998] to get the formulas).
Implications on the shared prosperity index
Theoretically, if the shared prosperity index is estimated from fitted values of the above parametric
Lorenz Curves, it will be different from the number estimated from microdata and original grouped
data. It is known that the parametric Lorenz Curve estimates the Lorenz curve and poverty rates quite
well although theoretically there should remain some differences between the actual Lorenz Curve and
the parametric Lorenz Curve due to the prediction error of the latter. As a result, if means of the poorest
40 percent of population are estimated from the parametric Lorenz Curve, they should be different
from the actual numbers. One of the objectives of this paper was to empirically examine whether the
differences can be non‐negligible.
Figure A1. Share of population from bottom 40 Figure A2. Share of population from bottom 40
percent across regions and type of welfare percent across regions and type of welfare
aggregate, Iraq 2012 aggregate, Rwanda 2010
80% 60%
60% 50%
40%
40%
30%
20%
20%
0%
10%
***Erbil
***Duhouk
Al‐Anbar
Babil
Wasit
Al‐Muthanna
***Missan
**Al‐Najaf
Suleimaniya
nominal real nominal real
Source: Microdata provided by regional focal points, members of the Global Poverty Working Group. Authors’ calculations.
24
Figure A3. Correlation between shares of negative numbers and absolute difference in shared
prosperity between income including and excluding negative numbers
Source: EUSILC, microdata provided by regional focal points, members of the Global Poverty Working Group. Authors’
calculations.
Notes: The difference in shared prosperity is calculated using scenario 1 (including negative numbers) and scenario 3
(excluding negative numbers).
25
Table A2. Shared prosperity numbers for three simulation scenarios (negative values) based on EUSILC
data
Country period SP with SP SP SP Absolute Absolute Absolute
negatives excluding excluding excluding difference, difference, difference,
and zero negative zero negative (2)‐(1) (3)‐(1) (4)‐(1)
(1) income (2) income (3) and zero
income (4)
Austria 2006‐2011 0.71 0.71 0.72 0.72 0.0 0.0 0.0
Belgium 2006‐2011 0.20 0.33 0.18 0.31 0.1 0.0 0.1
Bulgaria 2007‐2011 1.40 1.39 1.54 1.53 0.0 0.1 0.1
Cyprus 2006‐2011 ‐1.14 ‐1.14 ‐1.13 ‐1.13 0.0 0.0 0.0
Czech Republic 2006‐2011 1.85 1.85 1.85 1.85 0.0 0.0 0.0
Denmark 2006‐2011 ‐0.57 0.13 ‐0.57 0.13 0.7 0.0 0.7
Estonia 2006‐2011 1.40 1.51 1.44 1.55 0.1 0.0 0.1
Finland 2006‐2011 1.97 1.98 1.95 1.95 0.0 0.0 0.0
France 2006‐2011 3.30 3.15 3.30 3.15 0.1 0.0 0.1
Greece 2006‐2011 ‐6.16 ‐5.60 ‐5.91 ‐5.35 0.6 0.3 0.8
Hungary 2006‐2011 ‐0.54 ‐0.53 ‐0.53 ‐0.53 0.0 0.0 0.0
Iceland 2006‐2011 ‐3.09 ‐2.95 ‐3.09 ‐2.95 0.1 0.0 0.1
Ireland 2006‐2011 ‐3.90 ‐3.90 ‐3.76 ‐3.76 0.0 0.1 0.1
Italy 2006‐2011 ‐0.78 ‐0.98 ‐0.64 ‐0.85 0.2 0.1 0.1
Latvia 2006‐2011 0.35 0.51 0.27 0.40 0.2 0.1 0.1
Lithuania 2006‐2011 1.07 1.18 1.08 1.19 0.1 0.0 0.1
Luxembourg 2006‐2011 ‐1.70 ‐1.43 ‐1.70 ‐1.43 0.3 0.0 0.3
Netherlands 2006‐2011 1.18 0.92 1.18 0.92 0.3 0.0 0.3
Norway 2006‐2011 4.80 4.37 4.78 4.35 0.4 0.0 0.5
Poland 2006‐2011 5.62 5.60 5.59 5.56 0.0 0.0 0.1
Portugal 2006‐2011 0.07 0.07 0.07 0.07 0.0 0.0 0.0
Malta 2007‐2011 1.58 1.43 1.58 1.43 0.2 0.0 0.2
Romania 2006‐2011 3.40 3.57 3.40 3.57 0.2 0.0 0.2
Slovak Republic 2006‐2011 8.40 8.39 8.40 8.39 0.0 0.0 0.0
Slovenia 2006‐2011 1.47 1.39 1.47 1.39 0.1 0.0 0.1
Spain 2006‐2011 ‐0.81 ‐0.57 ‐0.62 ‐0.39 0.2 0.2 0.4
Sweden 2006‐2011 2.51 2.70 2.50 2.70 0.2 0.0 0.2
Switzerland 2007‐2011 2.04 1.92 2.04 1.92 0.1 0.0 0.1
United Kingdom 2006‐2011 ‐1.27 ‐1.02 ‐1.17 ‐0.93 0.2 0.1 0.3
Source: EUSILC, microdata provided by regional focal points, members of the Global Poverty Working Group. Authors’
calculations.
26
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Oseni, G., McGee, K., Dabalen, A., November 2014
6 Durable goods and poverty measurement
Amendola, N., Vecchi, G., November 2014
7 Inequality stagnation in Latin America in the aftermath of the global financial crisis
Cord, L., Barriga Cabanillas, O., Lucchetti, L., Rodriguez‐Castelan, C., Sousa, L. D., Valderrama, D. December
2014
8 Born with a silver spoon: inequality in educational achievement across the world
Balcazar Salazar, C. F., Narayan, A., Tiwari, S., January 2015
Updated on December 2016 by POV GP KL Team | 1
9 Long‐run effects of democracy on income inequality: evidence from repeated cross‐sections
Balcazar Salazar,C. F., January 2015
10 Living on the edge: vulnerability to poverty and public transfers in Mexico
Ortiz‐Juarez, E., Rodriguez‐Castelan, C., De La Fuente, A., January 2015
11 Moldova: a story of upward economic mobility
Davalos, M. E., Meyer, M., January 2015
12 Broken gears: the value added of higher education on teachers' academic achievement
Balcazar Salazar, C. F., Nopo, H., January 2015
13 Can we measure resilience? a proposed method and evidence from countries in the Sahel
Alfani, F., Dabalen, A. L., Fisker, P., Molini, V., January 2015
14 Vulnerability to malnutrition in the West African Sahel
Alfani, F., Dabalen, A. L., Fisker, P., Molini, V., January 2015
15 Economic mobility in Europe and Central Asia: exploring patterns and uncovering puzzles
Cancho, C., Davalos, M. E., Demarchi, G., Meyer, M., Sanchez Paramo, C., January 2015
16 Managing risk with insurance and savings: experimental evidence for male and female farm managers in
the Sahel
Delavallade, C., Dizon, F., Hill, R., Petraud, J. P., el., January 2015
17 Gone with the storm: rainfall shocks and household well‐being in Guatemala
Baez, J. E., Lucchetti, L., Genoni, M. E., Salazar, M., January 2015
18 Handling the weather: insurance, savings, and credit in West Africa
De Nicola, F., February 2015
19 The distributional impact of fiscal policy in South Africa
Inchauste Comboni, M. G., Lustig, N., Maboshe, M., Purfield, C., Woolard, I., March 2015
20 Interviewer effects in subjective survey questions: evidence from Timor‐Leste
Himelein, K., March 2015
21 No condition is permanent: middle class in Nigeria in the last decade
Corral Rodas, P. A., Molini, V., Oseni, G. O., March 2015
22 An evaluation of the 2014 subsidy reforms in Morocco and a simulation of further reforms
Verme, P., El Massnaoui, K., March 2015
Updated on December 2016 by POV GP KL Team | 2
23 The quest for subsidy reforms in Libya
Araar, A., Choueiri, N., Verme, P., March 2015
24 The (non‐) effect of violence on education: evidence from the "war on drugs" in Mexico
Márquez‐Padilla, F., Pérez‐Arce, F., Rodriguez Castelan, C., April 2015
25 “Missing girls” in the south Caucasus countries: trends, possible causes, and policy options
Das Gupta, M., April 2015
26 Measuring inequality from top to bottom
Diaz Bazan, T. V., April 2015
27 Are we confusing poverty with preferences?
Van Den Boom, B., Halsema, A., Molini, V., April 2015
28 Socioeconomic impact of the crisis in north Mali on displaced people (Available in French)
Etang Ndip, A., Hoogeveen, J. G., Lendorfer, J., June 2015
29 Data deprivation: another deprivation to end
Serajuddin, U., Uematsu, H., Wieser, C., Yoshida, N., Dabalen, A., April 2015
30 The local socioeconomic effects of gold mining: evidence from Ghana
Chuhan-Pole, P., Dabalen, A., Kotsadam, A., Sanoh, A., Tolonen, A.K., April 2015
31 Inequality of outcomes and inequality of opportunity in Tanzania
Belghith, N. B. H., Zeufack, A. G., May 2015
32 How unfair is the inequality of wage earnings in Russia? estimates from panel data
Tiwari, S., Lara Ibarra, G., Narayan, A., June 2015
33 Fertility transition in Turkey—who is most at risk of deciding against child arrival?
Greulich, A., Dasre, A., Inan, C., June 2015
34 The socioeconomic impacts of energy reform in Tunisia: a simulation approach
Cuesta Leiva, J. A., El Lahga, A., Lara Ibarra, G., June 2015
35 Energy subsidies reform in Jordan: welfare implications of different scenarios
Atamanov, A., Jellema, J. R., Serajuddin, U., June 2015
36 How costly are labor gender gaps? estimates for the Balkans and Turkey
Cuberes, D., Teignier, M., June 2015
37 Subjective well‐being across the lifespan in Europe and Central Asia
Bauer, J. M., Munoz Boudet, A. M., Levin, V., Nie, P., Sousa‐Poza, A., July 2015
Updated on December 2016 by POV GP KL Team | 3
38 Lower bounds on inequality of opportunity and measurement error
Balcazar Salazar, C. F., July 2015
39 A decade of declining earnings inequality in the Russian Federation
Posadas, J., Calvo, P. A., Lopez‐Calva, L.‐F., August 2015
40 Gender gap in pay in the Russian Federation: twenty years later, still a concern
Atencio, A., Posadas, J., August 2015
41 Job opportunities along the rural‐urban gradation and female labor force participation in India
Chatterjee, U., Rama, M. G., Murgai, R., September 2015
42 Multidimensional poverty in Ethiopia: changes in overlapping deprivations
Yigezu, B., Ambel, A. A., Mehta, P. A., September 2015
43 Are public libraries improving quality of education? when the provision of public goods is not enough
Rodriguez Lesmes, P. A., Valderrama Gonzalez, D., Trujillo, J. D., September 2015
44 Understanding poverty reduction in Sri Lanka: evidence from 2002 to 2012/13
Inchauste Comboni, M. G., Ceriani, L., Olivieri, S. D., October 2015
45 A global count of the extreme poor in 2012: data issues, methodology and initial results
Ferreira, F.H.G., Chen, S., Dabalen, A. L., Dikhanov, Y. M., Hamadeh, N., Jolliffe, D. M., Narayan, A., Prydz,
E. B., Revenga, A. L., Sangraula, P., Serajuddin, U., Yoshida, N., October 2015
46 Exploring the sources of downward bias in measuring inequality of opportunity
Lara Ibarra, G., Martinez Cruz, A. L., October 2015
47 Women’s police stations and domestic violence: evidence from Brazil
Perova, E., Reynolds, S., November 2015
48 From demographic dividend to demographic burden? regional trends of population aging in Russia
Matytsin, M., Moorty, L. M., Richter, K., November 2015
49 Hub‐periphery development pattern and inclusive growth: case study of Guangdong province
Luo, X., Zhu, N., December 2015
50 Unpacking the MPI: a decomposition approach of changes in multidimensional poverty headcounts
Rodriguez Castelan, C., Trujillo, J. D., Pérez Pérez, J. E., Valderrama, D., December 2015
51 The poverty effects of market concentration
Rodriguez Castelan, C., December 2015
52 Can a small social pension promote labor force participation? evidence from the Colombia Mayor
program
Pfutze, T., Rodriguez Castelan, C., December 2015
Updated on December 2016 by POV GP KL Team | 4
53 Why so gloomy? perceptions of economic mobility in Europe and Central Asia
Davalos, M. E., Cancho, C. A., Sanchez, C., December 2015
54 Tenure security premium in informal housing markets: a spatial hedonic analysis
Nakamura, S., December 2015
55 Earnings premiums and penalties for self‐employment and informal employees around the world
Newhouse, D. L., Mossaad, N., Gindling, T. H., January 2016
56 How equitable is access to finance in turkey? evidence from the latest global FINDEX
Yang, J., Azevedo, J. P. W. D., Inan, O. K., January 2016
57 What are the impacts of Syrian refugees on host community welfare in Turkey? a subnational poverty
analysis
Yang, J., Azevedo, J. P. W. D., Inan, O. K., January 2016
58 Declining wages for college‐educated workers in Mexico: are younger or older cohorts hurt the most?
Lustig, N., Campos‐Vazquez, R. M., Lopez‐Calva, L.‐F., January 2016
59 Sifting through the Data: labor markets in Haiti through a turbulent decade (2001‐2012)
Rodella, A.‐S., Scot, T., February 2016
60 Drought and retribution: evidence from a large‐scale rainfall‐indexed insurance program in Mexico
Fuchs Tarlovsky, Alan., Wolff, H., February 2016
61 Prices and welfare
Verme, P., Araar, A., February 2016
62 Losing the gains of the past: the welfare and distributional impacts of the twin crises in Iraq 2014
Olivieri, S. D., Krishnan, N., February 2016
63 Growth, urbanization, and poverty reduction in India
Ravallion, M., Murgai, R., Datt, G., February 2016
64 Why did poverty decline in India? a nonparametric decomposition exercise
Murgai, R., Balcazar Salazar, C. F., Narayan, A., Desai, S., March 2016
65 Robustness of shared prosperity estimates: how different methodological choices matter
Uematsu, H., Atamanov, A., Dewina, R., Nguyen, M. C., Azevedo, J. P. W. D., Wieser, C., Yoshida, N., March
2016
66 Is random forest a superior methodology for predicting poverty? an empirical assessment
Stender, N., Pave Sohnesen, T., March 2016
67 When do gender wage differences emerge? a study of Azerbaijan's labor market
Tiongson, E. H. R., Pastore, F., Sattar, S., March 2016
Updated on December 2016 by POV GP KL Team | 5
68 Second‐stage sampling for conflict areas: methods and implications
Eckman, S., Murray, S., Himelein, K., Bauer, J., March 2016
69 Measuring poverty in Latin America and the Caribbean: methodological considerations when estimating
an empirical regional poverty line
Gasparini, L. C., April 2016
70 Looking back on two decades of poverty and well‐being in India
Murgai, R., Narayan, A., April 2016
71 Is living in African cities expensive?
Yamanaka, M., Dikhanov, Y. M., Rissanen, M. O., Harati, R., Nakamura, S., Lall, S. V., Hamadeh, N., Vigil
Oliver, W., April 2016
72 Ageing and family solidarity in Europe: patterns and driving factors of intergenerational support
Albertini, M., Sinha, N., May 2016
73 Crime and persistent punishment: a long‐run perspective on the links between violence and chronic
poverty in Mexico
Rodriguez Castelan, C., Martinez‐Cruz, A. L., Lucchetti, L. R., Valderrama Gonzalez, D., Castaneda Aguilar,
R. A., Garriga, S., June 2016
74 Should I stay or should I go? internal migration and household welfare in Ghana
Molini, V., Pavelesku, D., Ranzani, M., July 2016
75 Subsidy reforms in the Middle East and North Africa Region: a review
Verme, P., July 2016
76 A comparative analysis of subsidy reforms in the Middle East and North Africa Region
Verme, P., Araar, A., July 2016
77 All that glitters is not gold: polarization amid poverty reduction in Ghana
Clementi, F., Molini, V., Schettino, F., July 2016
78 Vulnerability to Poverty in rural Malawi
Mccarthy, N., Brubaker, J., De La Fuente, A., July 2016
79 The distributional impact of taxes and transfers in Poland
Goraus Tanska, K. M., Inchauste Comboni, M. G., August 2016
80 Estimating poverty rates in target populations: an assessment of the simple poverty scorecard and
alternative approaches
Vinha, K., Rebolledo Dellepiane, M. A., Skoufias, E., Diamond, A., Gill, M., Xu, Y., August 2016
Updated on December 2016 by POV GP KL Team | 6
81 Synergies in child nutrition: interactions of food security, health and environment, and child care
Skoufias, E., August 2016
82 Understanding the dynamics of labor income inequality in Latin America
Rodriguez Castelan, C., Lustig, N., Valderrama, D., Lopez‐Calva, L.‐F., August 2016
83 Mobility and pathways to the middle class in Nepal
Tiwari, S., Balcazar Salazar, C. F., Shidiq, A. R., September 2016
84 Constructing robust poverty trends in the Islamic Republic of Iran: 2008‐14
Salehi Isfahani, D., Atamanov, A., Mostafavi, M.‐H., Vishwanath, T., September 2016
85 Who are the poor in the developing world?
Newhouse, D. L., Uematsu, H., Doan, D. T. T., Nguyen, M. C., Azevedo, J. P. W. D., Castaneda Aguilar, R. A.,
October 2016
86 New estimates of extreme poverty for children
Newhouse, D. L., Suarez Becerra, P., Evans, M. C., October 2016
87 Shedding light: understanding energy efficiency and electricity reliability
Carranza, E., Meeks, R., November 2016
88 Heterogeneous returns to income diversification: evidence from Nigeria
Siwatu, G. O., Corral Rodas, P. A., Bertoni, E., Molini, V., November 2016
89 How liberal is Nepal's liberal grade promotion policy?
Sharma, D., November 2016
90 CPI bias and its implications for poverty reduction in Africa
Dabalen, A. L., Gaddis, I., Nguyen, N. T. V., December 2016
91 Pro-growth equity: a policy framework for the twin goals
Lopez-Calva, L. F., Rodriguez Castelan, C., November 2016
92 Building an ex ante simulation model for estimating the capacity impact, benefit incidence, and cost
effectiveness of child care subsidies: an application using provider‐level data from Turkey
Aran, M. A., Munoz Boudet, A., Aktakke, N., December 2016
93 Vulnerability to drought and food price shocks: evidence from Ethiopia
Porter, C., Hill, R., December 2016
94 Job quality and poverty in Latin America
Rodriguez Castelan, C., Mann, C. R., Brummund, P., December 2016
Updated on December 2016 by POV GP KL Team | 7
For the latest and sortable directory,
available on the Poverty & Equity GP intranet site. http://POVERTY
WWW.WORLDBANK.ORG/POVERTY
Updated on December 2016 by POV GP KL Team | 8